CN113401107B

CN113401107B - Three-axis unmanned vehicle autonomous adjustment strategy and system in information collection process

Info

Publication number: CN113401107B
Application number: CN202110864033.0A
Authority: CN
Inventors: 徐小军; 孙承亮; 唐源江; 张国卿; 王立亚; 刘博龙; 潘迪博
Original assignee: National University of Defense Technology
Current assignee: National University of Defense Technology
Priority date: 2021-07-29
Filing date: 2021-07-29
Publication date: 2022-05-10
Anticipated expiration: 2041-07-29
Also published as: CN113401107A

Abstract

The invention discloses a three-axis unmanned vehicle autonomous adjustment strategy in an information collection process, which comprises the steps of obtaining remote information collection task information and real-time environment information; analyzing the remote information collection task information, and determining whether to adjust the load state and judge whether an external emergency exists or not based on the analysis result and the real-time environment information; executing an external emergency coping strategy step based on the judgment result that the external emergency exists; or, based on the judgment result, the three-axis unmanned vehicle is controlled to adjust the vehicle body state and the driving state by combining the real-time environment information. The invention also discloses a three-axis unmanned vehicle autonomous adjustment system in the information collection process. The invention can be well adapted to the field complex environment, ensures the smooth and effective execution of the information collection task, and improves the survival capability, the adaptability, the information collection capability and the protection capability of the vehicle.

Description

Three-axis unmanned vehicle autonomous adjustment strategy and system in information collection process

Technical Field

The invention relates to the technical field of unmanned combat systems, in particular to a three-axis unmanned vehicle autonomous adjustment strategy in an information collection process.

Background

The unmanned vehicle can be used in agriculture, industry, geology and other fields as an important component in future unmanned systems, and is used for performing field tasks such as crop irrigation, terrain exploration, geological mapping, patrol, information collection and the like.

The unmanned vehicle has strong task execution capability and better environment adaptability due to good maneuverability, stability, off-road property, safety and the like. In the process of executing tasks, people, field creatures, landforms and the like can influence the task execution condition of the unmanned vehicle.

The three-axis unmanned vehicle is an unmanned vehicle with three axles, comprises six wheels including a front axle, a middle axle and a rear axle, can adjust the height of a vehicle chassis and adjust the wheel base, and is provided with wheel-track switching wheels on the middle shafts of part of the three-axis unmanned vehicle, so that the three-axis unmanned vehicle has higher off-road property and stability.

In the task of information collection, the three-axis unmanned vehicle needs to hide itself to avoid being exposed by the discovery of the target while completing the collection of the target information, in the prior art, most three-axis unmanned vehicles are disguised by coloring (camouflage) in appearance, and a self-hiding effect is achieved by disguising, but based on the prior anti-information collection technology, the self-hiding mode has poor effect and is easy to be found, while some three-axis unmanned vehicles hide based on the external environments such as mountains, bushes, trenches, trees, caves, lakes and the like, the hiding mode is good, but during the information collection, the three-axis unmanned vehicle needs to move to find the information collection position and use the information collection equipment to collect the information, and when the information collection equipment is moved or used, because the information collection equipment of the unmanned operation platform is exposed, the three-axis unmanned vehicle is easy to be exposed, when the three-axis unmanned vehicle moves, the three-axis unmanned vehicle needs to pass through some special terrain areas, such as mountain roads, trenches, hills, slopes, puddles, swamps and the like, so as to be undetected, and the special terrain areas are very easy to cause the three-axis unmanned vehicle to overturn, sink or damage and the like, so that the information collection effect of the three-axis unmanned vehicle is influenced.

Meanwhile, the three-axis unmanned vehicle is also influenced by sudden conditions in the task execution process. When the existing three-axis unmanned vehicle faces a sudden situation, a better coping strategy is not provided, the existing three-axis unmanned vehicle is mostly hidden quickly to avoid the sudden situation, the coping strategy has a low success rate, and influences of various factors such as terrain, speed, gravity center, target speed, direction and type in the sudden situation are also considered, so that the coping strategy is difficult to realize, can only be used in a flat area basically, and cannot meet the use requirements of various outdoor environments.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provides a three-axis unmanned vehicle autonomous adjustment strategy in the information collection process.

The purpose of the invention is mainly realized by the following technical scheme: the three-axis unmanned vehicle autonomous adjustment strategy in the information collection process comprises the steps of obtaining remote information collection task information and real-time environment information; analyzing the remote information collection task information, and determining whether to adjust the information collection load state and judge whether an external emergency exists or not based on the analysis result and the real-time environment information; executing an external emergency coping strategy step based on the judgment result that the external emergency exists; or, based on the judgment result, the three-axis unmanned vehicle is controlled to adjust the vehicle body state and the driving state in combination with the real-time environment information; the vehicle body state adjusting method comprises the steps of lifting the tire pressure of a wheel, switching the wheel track structure of the wheel and adjusting the height of a vehicle chassis; the adjusting the driving state comprises determining a steering mode, adjusting a vehicle maneuver mode, and adjusting a vehicle driving silence state.

In the three-axis unmanned vehicle autonomous adjustment strategy, the real-time environment information at least comprises running environment information, vehicle running information, vehicle structure parameter information and external environment photoelectric information.

In the three-axis unmanned vehicle autonomous adjustment strategy, the external emergency is determined by an intelligent recognition and evaluation method, and the intelligent recognition and evaluation method comprises the following steps:

acquiring real-time environment information, and extracting target classification information of a target in a suspected emergency;

based on the target classification information, identifying suspected emergency conditions and evaluating the danger degree;

and determining whether the suspected emergency belongs to the external emergency based on the identification and the danger degree evaluation result.

In the three-axis autonomous adjustment strategy for the unmanned vehicle, the external emergency coping strategy comprises the following steps:

acquiring real-time environment information;

extracting target classification information of external burst conditions in real-time environment information;

determining a type of the emergency condition based on the target classification information;

controlling the three-axis unmanned vehicle to adjust the vehicle body state and the driving state based on the determination result and the real-time environment information;

the vehicle body state adjusting method comprises the steps of lifting the tire pressure of a wheel, switching the wheel track structure of the wheel and adjusting the height of a vehicle chassis; the adjusting the driving state comprises determining and switching a steering mode, adjusting a vehicle maneuvering mode and adjusting a vehicle driving silence state.

In the three-axis unmanned vehicle autonomous adjustment strategy, the target classification information at least comprises the position, the speed, the direction and the height of a target in an external emergency.

In the three-axis autonomous adjustment strategy for the unmanned vehicle, the determining the type of the emergency condition includes: when the suspected emergency is determined to belong to the external emergency, determining the type of the emergency based on the target classification information and the risk degree evaluation;

the burst condition type comprises at least one of a far lateral or oblique lateral burst condition, a near lateral or oblique lateral burst condition, a vertical or near vertical upper burst condition, and a vertical or near vertical lower burst condition.

In this three-axis unmanned vehicle is from the adjustment strategy, lift wheel tire pressure, switching wheel-track structure include:

if the road surface is soft or muddy, the front axle and the rear axle wheels of the three-axle unmanned vehicle reduce the tire pressure, and the middle axle switches the wheels to crawler-type running;

if the road surface is not soft or muddy, the tire pressure of the front axle and the rear axle of the three-axle unmanned vehicle is kept normal, and the middle axle switches the wheels into wheel type walking.

In the three-axis unmanned vehicle autonomous adjustment strategy, the method for judging a soft or muddy road comprises the following steps:

calculating the maximum horizontal shear force tau of the intermediate shaft wheel which can bear the ground when the intermediate shaft wheel travels on soft terrain_maxThe calculation formula is as follows:

wherein c is a constant, σ is a load borne by the ground,

is a shear angle;

obtaining the relation between the ground subsidence z and the load sigma borne by the ground, wherein the relation between the ground subsidence z and the load sigma borne by the ground is obtained by the following formula:

wherein b is the short side length of the contact area between the intermediate shaft wheel and the ground, namely the contact width, n is the index of soil deformation, k_cCohesion modulus for deformation of soil mass and

the coefficient of friction for soil deformation;

obtaining a relation of the load sigma borne by the ground through the conversion of the formula (2):

based on the vertical direction force balance of the middle shaft wheel when the soft terrain advances, a vertical direction balance formula is obtained:

in the formula, G is a vertical acting force, delta is an integral variable, l is a contact length, and delta M is an included angle between a contact point and a vertical central line of the wheel;

r' is the radius of the part in real time contact with the ground, and has:

in the formula, R_WRadius of parts for wheeled walking, R_TThe equivalent radius is the equivalent radius when the crawler-type walking is carried out, and alpha is the deformation angle of the wheel rim;

obtaining the amount z of subsidence of the intermediate shaft wheel on soft terrain under the same load condition through approximate processing based on the formulas (1) to (5)_MAnd the rim deformation angle α:

calculating the horizontal traction force F of the intermediate shaft wheel when the intermediate shaft wheel travels on soft terrain to obtain:

combining the formulas (1), (2), (3) and (7), obtaining the relationship between the maximum traction force F provided by the intermediate shaft wheel on soft terrain and the rim deformation angle alpha under the same load condition:

based on the formulas (1) to (8), the settlement Z with the same traction force required when the intermediate shaft wheels are respectively tracked walking and wheeled walking is solved_t；

Solving the actual subsidence Z of the intermediate shaft wheel during actual walking based on the formulas (1) to (8)_s；

And (3) comparison:

if Z is_s＞Z_tJudging that the road surface is soft or muddy, and adopting crawler-type walking;

if Z is_s＜Z_tAnd judging that the road surface is a non-soft or non-muddy road surface, and adopting a wheel type to walk.

In the three-axis autonomous adjustment strategy for the unmanned vehicle, the adjusting the vehicle driving silence state comprises: and selecting whether to run in a silent mode in which the engine is silent or not based on the remote information collection task information, the load detection information and the real-time environment information.

Compared with the prior art, the invention has the following beneficial effects: the invention can acquire remote information collection task information, load detection information and real-time environment information of the three-axis unmanned vehicle based on load equipment of the three-axis unmanned vehicle, and automatically adjust the vehicle body state and the running state of the vehicle based on the information, so that the vehicle can select a proper vehicle structure and a proper running mode according to terrain, road surface, emergency conditions and the like, and the vehicle can be more suitable for running in complex terrain, thereby hiding the vehicle by virtue of the complex terrain when executing an information collection task, being well suitable for the field complex environment, ensuring the smooth and effective execution of the information collection task, and improving the survival ability, the adaptability, the information collection ability and the protection ability of the vehicle.

Based on the above, the invention also discloses a three-axis unmanned vehicle autonomous adjusting system based on the information collection task, which comprises,

the acquisition module is used for acquiring remote information collection task information and real-time environment information;

the analysis module is used for collecting task information by remote information, and determining whether the information collection load state is adjusted and whether an external emergency condition exists or not based on the analysis result and the real-time environment information;

the system also comprises an execution module, a judgment module and a processing module, wherein the execution module is used for executing the step of the external emergency coping strategy based on the external emergency existing in the judgment result; or the control module is used for controlling the three-axis unmanned vehicle to adjust the vehicle body state and the running state based on the judgment result without an external emergency condition in combination with real-time environment information;

the vehicle body state adjusting method comprises the steps of lifting the tire pressure of a wheel, switching the wheel track structure of the wheel and adjusting the height of a vehicle chassis; the adjusting the driving state comprises determining a steering mode, adjusting a vehicle maneuver mode, and adjusting a vehicle driving silence state.

In the three-axis autonomous adjustment system for an unmanned vehicle, the control module includes:

the wheel-track control module is used for controlling the lifting of the tire pressure of the wheel and the switching of the wheel-track structure of the wheel;

the parking space control module is used for controlling the height adjustment of the vehicle chassis;

the steering control module is used for determining and switching a steering mode;

the driving control module is used for controlling the adjustment of the vehicle maneuvering mode;

an engine state control module controls a vehicle engine quiescent state.

The three-axis unmanned vehicle autonomous adjusting system based on the information collection task obtains corresponding information based on the obtaining module of the three-axis unmanned vehicle, and executes and controls the vehicle to adjust the vehicle state or the driving state based on the analysis result after the information is analyzed by the analysis module, so that the self-survival ability, the adaptive capacity, the information collection capacity and the protection capacity of the vehicle are improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:

FIG. 1 is a flow chart of a three-axis autonomous adjustment strategy for an unmanned vehicle during information collection;

FIG. 2 is a flow chart of step 102 in a three-axis unmanned vehicle autonomous adjustment strategy in an information collection process;

FIG. 3 is a flowchart of step 1022 in the three-axis unmanned vehicle autonomous adjustment strategy in the information collection process;

FIG. 4 is a flow chart of step 103 of the three-axis autonomous adjustment strategy for an unmanned vehicle during information collection;

FIG. 5 is a flow chart of step 1034 in a three-axis unmanned vehicle autonomous adjustment strategy during information collection;

FIG. 6 is a state diagram of the wheels in a three-axis drone vehicle center-steer mode;

FIG. 7 is a state diagram of the wheels of the three-axle drone vehicle in a fast steering mode;

FIG. 8 is a flow chart of step 104 in the three-axis unmanned vehicle autonomous adjustment strategy in the information gathering process;

FIG. 9 is a block diagram of a three-axis autonomous adjustment system for an unmanned vehicle during information collection;

FIG. 10 is a block diagram of the structure of the control module of the three-axis autonomous adjustment system of the unmanned vehicle during information collection;

FIG. 11 is a flow chart of a countermeasure against an external emergency when the external emergency is an emergency from a far lateral/oblique lateral direction;

FIG. 12 is a flow chart of an external emergency countermeasure strategy when the external emergency is an emergency from a close range lateral/diagonal;

FIG. 13 is a flow chart of an external burst handling strategy when the external burst is a burst from vertically/near vertically above;

FIG. 14 is a flow diagram of an external burst handling strategy when the external burst is a burst from a vertical/near vertical down;

fig. 15 is a specific embodiment of a three-axis autonomous adjustment strategy for an unmanned vehicle during information collection.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.

It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this disclosure and in the claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in the present invention to illustrate the operations performed by a system according to embodiments of the present invention. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

As shown in fig. 1, as a first embodiment of the present invention, the present invention discloses a three-axis unmanned vehicle autonomous adjustment strategy 100 in an information collection process, which is mainly used for vehicle autonomous adjustment of the three-axis unmanned vehicle when performing an information collection task, and specifically includes the following steps:

101. acquiring remote information collection task information and real-time environment information;

102. analyzing the remote information collection task information, and determining whether to adjust the information collection load state and judge whether an external emergency exists or not based on the analysis result and the real-time environment information;

103. executing an external emergency coping strategy step based on the judgment result that the external emergency exists;

or the like, or, alternatively,

104. controlling the three-axis unmanned vehicle to adjust the vehicle body state and the driving state based on the judgment result without an external emergency condition in combination with real-time environment information;

the vehicle body state adjusting method comprises the steps of lifting the tire pressure of a wheel, switching the wheel track structure of the wheel and adjusting the height of a vehicle chassis; the adjusting the driving state comprises determining a steering mode, adjusting a vehicle power mode and adjusting a vehicle driving silence state.

According to the three-axis unmanned vehicle autonomous adjustment strategy 100 in the information collection process, task and environment analysis is performed on remote information collection task information and real-time environment information of the three-axis unmanned vehicle, different handling and vehicle adjustment can be performed according to analysis results, and the survival capability, the adaptive capacity, the information collection capacity and the self-protection capacity of the three-axis unmanned vehicle are improved while the information collection task is ensured to be smoothly performed.

In step 101, the remote information collection task information is information collection task information issued remotely through a wireless network, and the information collection task information at least includes target information, position information, driving route information, and the like.

The real-time environment information at least comprises running environment information of the three-axis unmanned vehicle, vehicle running information, vehicle structure parameter information and external environment photoelectric information.

The driving environment information can be terrain information, map information, space information, obstacle information, road surface information, weather information and the like; the vehicle running information may be driving information, speed information, chassis height information, displacement information, coordinate information, direction information, vehicle slip information, wheel structure and stress information, etc. of the vehicle; the vehicle structure parameter information can be vehicle chassis height parameters, wheel tire pressure parameters, wheel state parameters, vehicle steering parameters, vehicle load parameters, vehicle inclination angle parameters and the like; the external environment photoelectric information can be external environment acousto-optic information, thermal imaging information, radiation information, electromagnetic wave information and the like.

When the remote information collection task information is acquired, the remote information collection task information can be acquired through vehicle-mounted equipment of the three-axis unmanned vehicle, such as a wireless transceiver and the like, when the real-time environment information is acquired, the vehicle-mounted equipment of the three-axis unmanned vehicle or an additionally-mounted acquisition unit, module or equipment is used for scanning, detecting and acquiring in real time, or the acquisition command is executed through guide information sent by a superior system of the three-axis unmanned vehicle and a received remote instruction to acquire. The acquisition unit, module or device may be a far infrared camera, a terrain scanner, a navigator, a gyroscope, a sensor, a locator, a velocimeter, a thermal imager, an electromagnetic wave detector, a radar, a visible light television viewing system, an infrared viewing system, a laser range finder, a tracking processor, a target identifier, etc.

Specifically, when the real-time environment information is acquired, a visible light television viewing system, an infrared viewing system, a laser range finder, a tracking processor, a target recognizer and the like can be used for acquiring visible light information/infrared radiation information of an external target and a background, photoelectric conversion is completed, original image data of the visible light/infrared target is formed and stored in an external memory, target capture and tracking can be completed by the tracking processor and the target recognizer, target angle deviation is measured and fed back in real time, distance measurement and target positioning are realized by a positioning instrument, a thermal imaging instrument, an electromagnetic wave detector, a radar and the like under a target stable tracking condition, and a data basis is provided for judgment of subsequent external burst conditions and adjustment of the structure and the state of a vehicle.

As shown in fig. 2, the step 102 specifically includes two analysis steps, specifically:

1021. analyzing the remote information collection task information, and determining whether to adjust the information collection load state or not based on the analysis result and the real-time environment information;

and the combination of (a) and (b),

1022. analyzing the remote information collection task information, and judging whether an external burst condition exists or not based on an analysis result and real-time environment information;

after receiving the remote information collection task information, the step 102 may determine an adjustment scheme for the information collection load on the three-axis unmanned vehicle by analyzing the remote information collection task information and the real-time environment information, and then determine that the information collection load can be protected during the driving and information collection processes, and the position is not exposed.

In step 1021, when the telematics task information includes the information collection load adjustment scheme, the information collection load adjustment is performed according to the information collection load adjustment scheme, and when the telematics task information does not include the information collection load adjustment scheme, the three-axis unmanned vehicle may perform autonomous real-time adjustment based on the analysis result and the real-time environment information. Specifically, after the real-time environmental information is analyzed, when the current information collecting load position influences the normal running of the vehicle and is exposed or damaged possibly, the information collecting load state can be adjusted by the three-axis unmanned vehicle, and can be load power, a working mode, a position, a height, an angle and the like.

Specifically, the information collecting load refers to various devices, modules, systems, and the like, which are arranged on the three-axis unmanned vehicle for the purpose of completing driving, information collection, reverse information collection, and the like, such as the above-mentioned far infrared camera, terrain scanner, navigator, gyroscope, sensor, locator, velometer, thermal imager, electromagnetic wave detector, radar, visible light television viewing system, infrared viewing system, laser range finder, tracking processor, target identifier, and the like. The mast is also arranged on part of the three-axis unmanned vehicle and used for carrying the above part of load and the sensing end of the load, such as a radar, a lens and the like.

As shown in fig. 3, when determining whether an external emergency exists in step 1022, the external emergency may be determined by a smart identification and evaluation method, where the smart identification and evaluation method includes the following steps:

10221. acquiring real-time environment information, and extracting target classification information of a target in a suspected emergency condition;

10222. identifying and evaluating the danger degree of the target in the suspected emergency condition based on the target classification information;

10223. and determining whether the suspected emergency belongs to the external emergency based on the identification and the danger degree evaluation result.

In this step 1022, by identifying and evaluating the risk degree of the classified target information of the target in the suspected emergency, it is determined whether the suspected emergency belongs to the external emergency, so that the subsequent steps can be performed according to the determination result.

Specifically, in step 10221, when a suspected emergency situation is found in the acquired real-time environment information, the target classification information of the target in the suspected emergency situation is extracted.

The suspected emergency refers to a situation that the three-axis unmanned vehicle encounters when performing a task and affects the driving of the three-axis unmanned vehicle or the task, and the situation is an emergency that may cause the three-axis unmanned vehicle to collide, damage, overturn, and the like based on an external environment (target), where the external environment may be any object in the air, on the ground, and in the ground, such as a high-speed and low-speed moving object, an object having radiation, thermal imaging reaction, or biological reaction, and various obstacles and the like. The object classification information at least comprises information of speed, direction, height, distance, angle, position, size and the like of the object in the external emergency. Furthermore, the target classification information may further include information such as energy, volume, and motion trajectory of the target in the external burst condition, so as to improve the state information of the target in the external burst condition, and use the state information as an information basis or judgment content, thereby improving the accuracy of determining the subsequent external burst condition.

Specifically, in step 10222, the suspected emergency condition identification and risk level evaluation may be performed based on the control system of the three-axis unmanned vehicle, or the data may be transmitted to a remote end through the network module, and the identification and evaluation may be performed remotely, and the subsequent steps may be performed through a remote command.

It should be noted that the target identification technology is already common in the prior art, and the embodiment is not redundantly described, as a feasible way: the suspected emergency recognition can be implemented by performing pre-training (also called initial training) by adopting a large visual data set such as ImageNet and KITTI, accurately constructing characterization modes of different types of targets through deep neural network and small sample training described by hierarchical parameters, realizing significant region extraction, potential target prediction, target classification and position regression in a single-frame image by utilizing a lightweight deep network, performing post-training and performance evaluation by adopting measured data, classifying and recognizing multiple types of targets, and completing the suspected emergency recognition.

Specifically, in step 10223, after the identification and the corresponding risk level evaluation are completed, if a suspected emergency threatens the driving and self-safety of the three-axis unmanned vehicle, the three-axis unmanned vehicle may be determined as an external emergency.

As shown in fig. 4, step 103 is mainly directed to a three-axis autonomous adjustment strategy for an unmanned vehicle when an external emergency exists after an information collection task is analyzed, so as to avoid a task failure caused by the external emergency when the information collection task is executed.

Specifically, in step 103, the detailed external emergency countermeasure procedure includes:

1031. acquiring real-time environment information;

1032. extracting target classification information of targets in external burst conditions in real-time environment information;

1033. determining a type of the emergency condition based on the target classification information;

1034. controlling the three-axis unmanned vehicle to adjust the vehicle body state and the driving state based on the determination result and the real-time environment information;

the vehicle body state adjusting comprises the steps of lifting the tire pressure of the vehicle wheel, switching the wheel-track structure of the vehicle wheel and adjusting the height of a chassis of the vehicle; the adjusting the driving state comprises determining and switching a steering mode, adjusting a vehicle maneuvering mode and adjusting a vehicle driving silence state.

This step 103 can be based on the external real-time environmental information of the three-axis unmanned vehicle, determine the emergency and adjust the vehicle body state and the driving state according to the threat type, and further enable the vehicle to select a suitable vehicle structure and driving mode according to the terrain, the road surface, the emergency type, etc., select different vehicle structures and driving modes for different emergency, pertinently solve the emergency, and adjust through vehicle structure and driving mode, so that the vehicle can more adapt to the driving of complex terrain, thereby having a wider space and environment when dealing with the emergency, improving the vehicle viability and adaptability, and the vehicle acquires the environmental data in real time, the vehicle structure and driving mode are synchronously performed after determining the emergency, and further improving the response speed, and the success rate is high.

Specifically, in step 1031 and step 1032, the target classification information for extracting the real-time environment information and the external emergency in the real-time environment information may be obtained by using the structures and manners of step 101 and step 10221, which is not described herein too much, and after corresponding data has been obtained in step 101 and step 10221, step 1031 and step 1032 may be directly skipped in this step 103.

And in step 1033, said determining the type of emergency condition comprises:

when the suspected emergency is determined to belong to the external emergency, determining the type of the emergency based on the target classification information and the risk degree evaluation; the burst condition type comprises at least one of a far lateral or oblique lateral burst condition, a near lateral or oblique lateral burst condition, a vertical or near vertical upper burst condition, and a vertical or near vertical lower burst condition.

The determination of the burst condition type can be determined based on the height information, angle information and position information of the target in the target classification information, and can be used for risk degree assessment based on thermal imaging information, speed information, photoelectric information and the like of the target, such as threat of some external landforms (trenches, hills and the like), can be assessed as a low-risk burst condition, and some high-speed moving objects and objects with high radiation energy can be assessed as a high-risk burst condition.

Specifically, the target in the long-distance lateral or oblique lateral emergency, the short-distance lateral or oblique lateral emergency, the vertical or near vertical upper emergency may be an external tree, a mountain, a missile, an enemy, an animal, a trap, a tank, a armored car, a rock fall, etc., and the target in the vertical or near vertical lower emergency may be a trench, a mine, a raised obstacle (a stone, a hill, a humus), etc.

Step 1034 is mainly used for controlling the three-axis unmanned vehicle to perform corresponding vehicle body state and driving state adjustment based on the emergency type and the real-time environment information, so as to solve the problem of external emergency with pertinence, safety and stability.

As shown in fig. 5, step 1034 mainly includes a vehicle body state adjusting step 10341 and a running state adjusting step 10342 of the vehicle.

Specifically, the vehicle body state adjusting step 10341 mainly includes lifting the tire pressure of the wheel, switching the wheel-track structure of the wheel, and adjusting the height of the vehicle chassis.

Further, lift wheel tire pressure, switch wheel track structure mainly include: if the road surface is soft or muddy, the front axle and the rear axle wheels of the three-axle unmanned vehicle reduce the tire pressure, and the middle axle switches the wheels to crawler-type running; if the road surface is not soft or muddy, the tire pressure of the front axle and the rear axle of the three-axle unmanned vehicle is kept normal, and the middle axle switches the wheels into wheel type walking. Through this step regulation, when the road surface is soft or muddy road surface, can increase adhesive force through reducing front and rear axle wheel tire pressure to switch into the crawler-type walking with the jackshaft wheel, increase the wheel middle part and support area, reduce rolling resistance, avoid the automobile body to sink, and then can be fast, steadily advance and can not appear skidding, sink.

It should be noted that, in order to realize the switching between the crawler type and the wheel type structure, the wheel of the intermediate shaft of the three-shaft unmanned vehicle may be selected from the existing wheel-track switching type wheel structure to realize the switching between the wheel type and the crawler type structure.

It should be noted that: the triaxial unmanned vehicle has the advantages that the contact area between the intermediate shaft wheels and the ground is small in the wheel type mode, the resistance is small when the triaxial unmanned vehicle travels on flat and solid terrain, the speed is high, the efficiency is high, and the support passing characteristic of the triaxial unmanned vehicle under soft and slippery terrain is poor, so that the triaxial unmanned vehicle is prone to sinking and slipping. After the wheel type mode is converted into the crawler type mode, the contact mode between the wheels of the intermediate shaft and the ground is changed from point contact to surface contact, the ground contact area can be effectively increased, the ground contact specific pressure is reduced, and therefore the bearing passing performance of the unmanned vehicle on soft and slippery terrain is improved. And because the intermediate shaft wheel generates shearing force through pressure and traveling power applied to the ground, the ground applies corresponding supporting force and traction force to the intermediate shaft wheel. Because the three-axis unmanned vehicle has different driving modes, the intermediate shaft wheels have different shapes and different contact conditions with the ground. Under the soft topography, still can produce the subsidence when receiving the pressure of wheel on ground, when the settlement volume is big to a certain extent, the unmanned car of triaxial just can not pass through smoothly.

Therefore, the three-axis unmanned vehicle is switched between a wheel type advancing mode and a crawler type advancing mode by designing the middle shaft wheels into a wheel-track switching structure so as to meet the driving requirements of different road surfaces, ensure that the three-axis unmanned vehicle can normally drive on soft or muddy road surfaces, and reasonably judge whether the driving road surface of the vehicle is soft or muddy road surfaces.

Based on this, the present embodiment provides the following determination method for determining whether the road surface where the three-axis unmanned vehicle is located is a soft or muddy road surface based on the contact model of the deformation wheel established by the beck theory with the ground in different modes:

wherein c is a constant, σ is a load borne by the ground,

is a shear angle;

the coefficient of friction for soil deformation;

r' is the radius of the part in real time contact with the ground, and has:

And (3) comparison:

The running road condition of the three-axis unmanned vehicle can be judged by the soft or muddy road judging method, so that the vehicle structure is adjusted based on the road condition, and the maneuverability and feasibility of the three-axis unmanned vehicle are ensured.

Further, adjusting the vehicle chassis height mainly includes: based on the type of emergency, the vehicle chassis height is raised or lowered to keep the vehicle body in high, medium or low drive. In the embodiment, the height of the chassis can be adjusted according to the requirements of the stability and the maneuverability of the vehicle, and when the three-axis unmanned vehicle is in continuous steering, oblique running or in a sudden situation facing to the transverse direction/oblique transverse direction, the chassis can be reduced to be driven to a low position, the gravity center of the vehicle body is reduced, and the stability of the vehicle is improved; when the linear rapid maneuvering is in a sudden situation facing the vertical/near-vertical upper part, the chassis can be adjusted to run to a normal middle position, and the maneuverability of the chassis is improved; when the vehicle is in a straight line rapid maneuvering and faces a sudden situation below the vertical/near vertical direction, the chassis can be improved to be driven to a high position, so that the chassis of the vehicle is far away from the threat, and the damage is reduced.

It should be noted that, the height of the vehicle chassis can be adjusted by installing a lifting device such as a hydraulic cylinder between the axle and the wheel, the telescopic end of the hydraulic cylinder is connected with the wheel, and the wheel is connected with a hub motor or a motor, so that the wheel is driven to lift by the telescopic of the hydraulic cylinder, the chassis can be lifted, and the driving of the wheel is not influenced in the lifting process.

The driving state adjustment step 10342 generally comprises determining and switching a steering mode, adjusting a vehicle maneuver mode, and adjusting a vehicle driving silence state, wherein determining and switching a steering mode comprises: determining whether to adopt a central steering mode to quickly steer to steer a defensive surface of the side of the vehicle body to an incoming direction based on the type of the emergency; or, determining whether to assist the vehicle in maneuvering using the fast steering mode. Adjusting the vehicle maneuver mode includes: based on the emergency type, the vehicle driving state is adjusted to a fast crab running mode, a fast S-shaped running mode, or a fast straight running mode. Furthermore, in order to further reduce the moving and the static caused by the vehicle, the running silent state of the vehicle can be adjusted in the step, and whether the engine runs normally or runs in the silent state is selected according to the information collection task and the external environment information so as to reduce the working sound.

Specifically, as shown in fig. 6, the center steering mode is that the vehicle rotates clockwise or counterclockwise along the center.

As shown in fig. 7, the fast steering mode is: the front wheels rotate by a required angle along the steering direction, the direction of the middle wheels is unchanged, and the rear wheels rotate by the same angle along the steering direction in the reverse direction, so that four-wheel steering is realized by the front wheels and the rear wheels, the steering radius is smaller, the steering sensitivity is higher, and the steering speed is high.

In some embodiments, when facing a long range lateral/oblique lateral emergency, a center steering mode may be used for fast steering to steer the defensive side of the body side to the direction of the incoming, and a fast steering mode may be used for fast maneuvers in a fast S-shaped travel mode. In some embodiments, when facing short range lateral/oblique lateral emergencies, rapid steering may be used in a center steering mode to steer the defensive side of the body side into the oncoming direction and rapid maneuvers may be used in a rapid crab mode. In some embodiments, when facing an emergency situation above or below the vertical/near hammer, the fast steering mode may be employed to maneuver quickly in a fast straight mode.

As shown in fig. 8, step 104 is mainly used to adjust the vehicle policy of the three-axis unmanned vehicle when there is no external emergency as a result of the determination, so as to ensure smooth performance of the information collection task.

Specifically, step 104 mainly includes a vehicle body state adjustment step 1041 and a driving state adjustment step 1042 of the vehicle.

The step 1041 of adjusting the vehicle body state mainly comprises the steps of lifting the tire pressure of the wheel, switching the wheel-track structure of the wheel and adjusting the height of the chassis of the vehicle. The method for elevating the tire pressure, switching the wheel-track structure and adjusting the chassis height of the vehicle is described in detail in step 103, and will not be redundantly described here. It should be noted that, in this step, the tire pressure of the lifting wheel and the wheel track structure of the switching wheel can be adjusted based on the road surface condition, and the height of the vehicle chassis in this step can be adjusted by considering, in addition to the above, that the mountain stones, rotten wood, ground protrusions and the like encountered during the traveling process can be regarded as vertical/near-vertical lower sudden conditions, so as to further keep the vehicle chassis away from the threat and reduce the damage.

The driving state adjusting step 1042 generally includes determining a steering mode, adjusting a vehicle maneuver mode, and adjusting a vehicle driving silence state.

It should be noted that the principle and manner of determining the steering mode and adjusting the vehicle maneuver mode have been described above, and in this step, since the steering mode is used for the information collection task and there is no external threat, it is preferable that the steering mode is directly determined as the normal steering mode, and the vehicle maneuver mode is performed in the normal driving mode, so as to reduce the large sound or environmental change caused by the high speed or fast motion of the vehicle and avoid the exposure.

As shown in fig. 9, a second embodiment of the present invention provides a three-axis autonomous adjusting system 200 for an unmanned vehicle during information collection, comprising,

an obtaining module 201, configured to obtain remote information collection task information and real-time environment information;

the analysis module 202 is used for collecting task information remotely, and determining whether to adjust the position of an information collection load and judge whether an external emergency exists or not based on an analysis result and real-time environment information;

also comprises a step of adding a new type of additive,

an executing module 203, configured to execute an external emergency handling policy step based on the external emergency existing in the determination result;

or the like, or, alternatively,

the control module 204 is used for controlling the three-axis unmanned vehicle to adjust the vehicle body state and the running state based on the judgment result without an external emergency condition and in combination with real-time environment information;

The three-axis unmanned vehicle autonomous adjustment system 200 in the information collection process obtains the remote information collection task information and the real-time environment information through the obtaining module 201 and the analyzing module 202, analyzes the information, so that the steps of an external emergency coping strategy are executed through the execution module 203 or the three-axis unmanned vehicle is controlled through the control module 204 to adjust the vehicle body state and the driving state, further, the vehicle body state and the running state of the vehicle are adjusted, so that the vehicle can select a proper vehicle structure and a proper running mode according to the terrain, the road surface, the sudden situation and the like, the vehicle can be more suitable for running on complex terrain, therefore, when the information collection task is executed, the vehicle can hide the vehicle by means of the complex terrain, can well adapt to the complex environment in the field, ensures the smooth and effective execution of the information collection task, and improves the survival capability, the adaptability, the information collection capability and the protection capability of the vehicle.

As shown in fig. 10, when actually used, the control module 204 may be built based on an autonomous driving controller of the three-axis unmanned vehicle itself, so as to supplement the autonomous driving controller, and for facilitating the classified control of the control module 204, it may be composed of the following modules:

the wheel-track control module 2041 is used for controlling the lifting of the tire pressure of the wheels and the switching of wheel-track structures of the wheels;

the parking space control module 2042 is used for controlling the height adjustment of a vehicle chassis;

a steering control module 2043 for determining and switching a steering mode;

a drive control module 2044 for controlling vehicle mobility mode adjustment;

an engine state control module 2045 for controlling the vehicle engine quiet state.

When the vehicle body state needs to be adjusted, the tire pressure, the wheel state switching and the chassis height switching can be respectively controlled by the wheel track control module 2041 and the parking space control module 2042, when the driving state needs to be adjusted, the steering mode can be determined and controlled by the steering control module 2043, the vehicle maneuvering mode adjustment is controlled by the driving control module 2044, further classification control is performed, the control precision and the response speed are improved, and when the engine state needs to be adjusted, whether the engine is in a silent state or not can be controlled by the engine state control module 2045.

It should be noted that, when necessary, information may also be transmitted and received by the remote signal transmitting and receiving device of the autonomous driving controller, so as to implement remote control.

It should be noted that, in the above coping strategy for the external emergency, the corresponding coping system for the external emergency may be composed of modules in the three-axis autonomous adjustment system 200 of the unmanned vehicle in the information collection process, such as an acquisition module, a control module, a wheel track control module, a parking space control module, a steering control module, a driving control module, and the like, so as to realize the same adjustment of the vehicle body structure and state, and the three-axis autonomous adjustment system 200 of the unmanned vehicle in the information collection process may be directly used for the coping system for the external emergency, so that the two systems share the system, thereby reducing the load capacity of the three-axis unmanned vehicle.

In summary, in order to better implement the coping strategy for the external emergency, the following explains and explains the coping strategy for the external emergency in detail on the basis of the three-axis autonomous adjusting system 200 of the unmanned vehicle in the information collecting process, in combination with embodiments 1 to 4.

Detailed description of the preferred embodiment 1

As shown in fig. 11, the coping strategy based on the external emergency specifically includes:

and acquiring real-time environment information. The real-time environment information acquisition equipment can be a far infrared camera, a terrain scanner, a navigator, a gyroscope, a sensor, a position finder, a speed meter, a thermal imager, an electromagnetic wave detector, a radar, a visible light television viewing and aiming system, an infrared viewing and aiming system, a laser range finder, a tracking processor, a target recognizer and the like.

And extracting target classification information of targets in external burst conditions in the real-time environment information. The object classification information at least comprises information of speed, direction, height, distance, angle, position, size and the like of the object in the external emergency.

Determining the type of the emergency as a long-distance transverse/oblique transverse attack based on the target classification information;

controlling the three-axis unmanned vehicle to adjust the vehicle body state and the driving state;

the vehicle body state adjusting comprises the steps of lifting the tire pressure of the vehicle wheel, switching the wheel-track structure of the vehicle wheel and adjusting the height of a chassis of the vehicle; the adjusting the driving state comprises determining and switching a steering mode and adjusting a vehicle maneuvering mode.

When the adjustment is carried out, when the road surface is soft or muddy, the wheel-track control module controls the front axle wheels and the rear axle wheels to reduce the tire pressure, and the middle axle wheels are switched to a crawler type walking mode to walk; when the road surface is not soft or muddy, the wheel-track control module controls the front and rear axle wheels to keep normal tire pressure, and the middle axle wheels are switched to a wheel type walking mode to walk.

And simultaneously, controlling the height of the chassis of the vehicle to be lowered to the lowest position, and driving according to a low-position driving state.

After the completion, determining as a central steering mode to perform vehicle body steering, enabling the vehicle body defense surface of the three-axis unmanned vehicle to face the threat direction, quickly maneuvering by an S-shaped trajectory, and switching the vehicle into a quick steering mode;

and finally, controlling the vehicle maneuvering mode to be a rapid S-shaped traveling mode, and rapidly maneuvering by an S-shaped trajectory to finish the adjustment of the whole three-axis unmanned vehicle to the vehicle in an external emergency.

Specific example 2

As shown in fig. 12, the coping strategy based on the external emergency specifically includes:

Determining the type of the emergency as a short-distance transverse/oblique transverse attack based on the target classification information;

the system is used for controlling the three-axis unmanned vehicle to adjust the vehicle body state and the running state;

the vehicle body state adjusting method comprises the steps of lifting the tire pressure of a wheel, switching the wheel track structure of the wheel and adjusting the height of a vehicle chassis; the adjusting the driving state comprises determining and switching a steering mode and adjusting a vehicle maneuvering mode.

After the completion, determining the three-axis unmanned vehicle as a center steering mode to perform vehicle body steering, enabling the vehicle body defense surface of the three-axis unmanned vehicle to face the threat direction, and switching the vehicle into a rapid crab running mode;

and finally, controlling the vehicle maneuvering mode to be a quick crab-walking mode, and quickly maneuvering by using the crab-walking trajectory line to finish the adjustment of the whole three-axis unmanned vehicle for responding to the vehicle under the external emergency.

Specific example 3

As shown in fig. 13, the strategy for coping with an external emergency specifically includes:

Determining the type of the emergency as a vertical/near-vertical upper attack based on the target classification information;

And simultaneously, controlling the height of the chassis of the vehicle to be reduced to a middle position, and driving according to a middle driving state.

After the steering is finished, determining to be in a fast steering mode for fast steering;

and finally, controlling the vehicle maneuvering mode to be a rapid straight-driving mode, and rapidly maneuvering by using a straight-driving trajectory line to finish the adjustment of the whole three-axis unmanned vehicle for the vehicle under the external emergency.

Specific example 4

As shown in fig. 14, the coping strategy based on the external emergency specifically includes:

Determining the type of the emergency situation as a vertical/near-vertical lower attack based on the target classification information;

And simultaneously, controlling the height of the vehicle chassis to rise to the highest position, and driving according to a high-position driving state.

Similarly, for better understanding and implementation, the three-axis unmanned vehicle autonomous adjustment strategy 100 in the information collection process and the three-axis unmanned vehicle autonomous adjustment system 200 in the information collection process will be described in further detail with reference to specific example 5.

Specific example 5

As shown in fig. 15, the three-axis autonomous adjustment strategy for the unmanned vehicle in the information collection process includes the following steps:

acquiring remote information collection task information and real-time environment information through an acquisition module;

the analysis module analyzes the remote information collection task information, determines whether the height of the mast is adjusted or not by combining with the real-time environment information, and judges whether an external emergency exists or not;

wherein the content of the first and second substances,

executing an external emergency coping strategy step when an external emergency exists based on the judgment result;

when no external emergency exists based on the judgment result, the three-axis unmanned vehicle is controlled to adjust the vehicle body state and the driving state by combining with the real-time environment information; the adjustment of the vehicle body state comprises the steps of lifting the tire pressure of the wheels, switching the wheel track structure of the wheels and adjusting the height of a chassis of the vehicle; the adjusting the driving state comprises determining a steering mode, adjusting a vehicle maneuver mode, and adjusting a vehicle driving silence state.

During adjustment, the wheel-track control module controls the lifting of the tire pressure of the wheel and the switching of the wheel-track structure of the wheel; when the road surface is soft or muddy, the wheel-track control module controls the front and rear axle wheels to reduce the tire pressure, and the middle axle wheels are switched to a crawler type walking mode to walk; when the road surface is not soft or muddy, the wheel-track control module controls the front and rear axle wheels to keep normal tire pressure, and the middle axle wheels are switched to a wheel type walking mode to walk.

Meanwhile, the parking space control module selects whether to control the vehicle chassis to adjust the height according to the road condition, the gradient, the speed and the like of the road vehicle.

The steering control module determines that the vehicle is in a normal steering mode to steer the vehicle body;

and finally, the driving control module controls the vehicle maneuvering mode to be a normal driving mode, and the vehicle drives in a normal state to complete the whole vehicle structure and state adjustment.

And when the engine state needs to be controlled during adjustment, the engine state control module controls the vehicle engine to normally run or run in a silent mode.

The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. The three-axis unmanned vehicle autonomous adjustment strategy in the information collection process is characterized by comprising the following steps of,

acquiring remote information collection task information and real-time environment information;

analyzing the remote information collection task information, and determining whether to adjust the information collection load state and judge whether an external emergency exists or not based on the analysis result and the real-time environment information;

executing an external emergency coping strategy step based on the judgment result that the external emergency exists, wherein the external emergency coping strategy step comprises the following steps:

acquiring real-time environment information;

extracting target classification information of targets in external burst conditions in real-time environment information;

or the like, or, alternatively,

controlling the three-axis unmanned vehicle to adjust the vehicle body state and the driving state based on the judgment result without an external emergency condition in combination with real-time environment information;

the vehicle body state adjusting comprises the steps of lifting the tire pressure of the vehicle wheel, switching the wheel-track structure of the vehicle wheel and adjusting the height of a chassis of the vehicle; the adjusting the driving state comprises determining a steering mode, adjusting a vehicle maneuver mode, and adjusting a vehicle driving silence state.

2. The three-axis unmanned vehicle autonomous adjustment strategy as claimed in claim 1, wherein the real-time environment information at least comprises driving environment information, vehicle driving information, vehicle structure parameter information and external environment photoelectric information.

3. The three-axis unmanned vehicle autonomous coordination strategy in information collection according to claim 1, characterized in that the external emergency is determined by a smart identification evaluation method, the smart identification evaluation method comprising:

4. The tri-axial unmanned vehicle autonomous coordination strategy in information collection process of claim 1, wherein the target classification information comprises at least position, speed, direction and height of target in external emergency.

5. The tri-axial drone vehicle autonomous reconciliation strategy in information collection process of claim 1 wherein the determining of the type of incident includes: when the suspected emergency is determined to belong to the external emergency, determining the type of the emergency based on the target classification information and the risk degree evaluation;

the burst type includes at least one of a long-distance lateral or diagonal burst, a short-distance lateral or diagonal burst, a vertical or near-vertical up-burst, and a vertical or near-vertical down-burst.

6. The three-axis unmanned vehicle autonomous adjustment strategy of claim 1, wherein the tire pressure of the lifting wheel and the wheel track structure of the switching wheel comprise:

7. The three-axis unmanned vehicle autonomous adjustment strategy in information collection according to claim 6, wherein the method for determining soft or muddy road comprises:

wherein c is a constant, σ is a load borne by the ground,

is a shear angle;

the coefficient of friction for soil deformation;

where G is the vertical force, δ is the integral variable, l is the contact length, δ_MIs the included angle between the contact point and the vertical central line of the wheel;

r' is the radius of the part in real time contact with the ground, and has:

based on the formulas (1) to (5), the sinking of the intermediate shaft wheel on soft terrain under the same load condition is obtained through approximate processingQuantity z_MAnd the rim deformation angle α:

And (3) comparison:

8. The three-axis unmanned vehicle autonomous coordination strategy of claim 1, wherein said adjusting vehicle driving silence state comprises:

and selecting whether to run in a silent mode in which the engine is silent or not based on the remote information collection task information, the load detection information and the real-time environment information.

9. The three-axis unmanned vehicle autonomous adjustment system in the information collection process is characterized by comprising,

the analysis module is used for analyzing the remote information collection task information, and determining whether the information collection load state is adjusted and whether an external emergency exists or not based on the analysis result and the real-time environment information;

also comprises the following steps of (1) preparing,

the execution module is used for executing the strategy step of coping with the external burst condition based on the external burst condition existing in the judgment result;

or the like, or, alternatively,

the control module is used for controlling the three-axis unmanned vehicle to adjust the vehicle body state and the running state based on the judgment result without an external emergency condition and in combination with real-time environment information;